It is not uncommon for businesses, governmental organizations, and other enterprises to maintain many different kinds of electronic data from disparate sources. For example, health-oriented enterprises will often maintain patient information from many sources, including scanned documents, electrocardiograms, X-rays, MRI scans and other medical imaging procedures, lab results, dictated reports of surgery, as well as patient demographics and contact information. There are challenges associated with providing a computing platform that enables customized retrieval and display of data across so many different kinds of data from so many different sources.
Some of the challenges arise from the fact that, at any given point in time, traditional databases generally will only exist in a single physical expression (e.g., only one table structure implementation). However, the pattern of data consumption is ideally malleable by consumer, as each consuming client has variant needs. The situation is further complicated by the fact that the number of data consumers is likely to grow over time. Each consumer generally requires a consistent data consumption profile regardless of any change over time to the original data sources. Currently, changes to upstream source systems create exponential amounts of derivative work necessary to update and support consuming systems.
The discussion above is merely provided for general background information and is not intended for use as an aid in determining the scope of the claimed subject matter.